Substance abuse is a disorder that has been associated with a compromise of reward learning and decision making mechanisms in the brain, notably, the midbrain dopamine system and its striatal targets. Prominent theories suggest that drugs of abuse interact with dopamine, "hijacking" normal learning and instead directing behavior towards the procurement and consumption of the drug. In recent years, the computational framework of reinforcement learning has been leveraged to make great strides in understanding the role of dopamine in reward learning and decision making, and how slight modifications of its signals could lead to such detrimental effects. Reinforcement learning models of decision making describe how basal-ganglia structures learn to evaluate different stimuli in terms of their future reward value, and how dopaminergic activity affects such learning processes. But how does the brain identify which, of all the available stimuli, are the relevant ones to represent and evaluate? This "representation learning" problem has been largely ignored in both the experimental and the computational literature, and may lie at the heart of substance abuse disorder. Substance abusers are not wholly irrational decision makers;in fact, research shows that they employ normal economic decision making to the purchase of drugs of abuse. Nevertheless, a clear abnormality is their fixation on stimuli predicting drug rewards (and their sometimes great creativity in learning to manipulate these to obtain drugs) to the exclusion of consideration of predictors of alternative rewards such as salary from holding a job and the support of one's family. This proposal is motivated by the hypothesis that this skewed attention may be the result of an abnormal representation learning process that causes an over-representation of drug-reward predicting cues. The goal of this proposal is to carry out behavioral and fMRI investigations of the computational and neural basis of representation learning and its interaction with reward learning in the human brain. The studies will employ a novel decision making task that has been specifically designed to highlight this interaction. The hypothesis to be tested is that the prefrontal cortex constructs representations of the world, identifying and directing attention to stimulus dimensions that are relevant for the task at hand, and constructing representations that can be used by the basal ganglia in the process of reinforcement learning. Moreover, we hypothesize that dopaminergic circuitry and related prediction error signals mediate the interaction between representation learning in the prefrontal cortex and reinforcement learning in the basal ganglia. The research proposed will provide initial testing of these hypotheses by detecting neural signals related to representation learning and uncovering the computational strategies by which representation learning proceeds in humans. Understanding representation learning processes, their realization in neural circuitry, and how they are influenced by drug-sensitive neuromodulators such as dopamine, will inform theories of what it is that goes awry in drug-influenced decision making, and provide new directions for diagnosis and treatment of substance abuse. PUBLIC HEALTH RELEVANCE: Substance abuse is a serious problem of public health that centers around the brain's decision-making mechanisms for obtaining rewards. While much research has concentrated on how drugs of abuse might alter reward learning mechanisms, little attention has been devoted to the more fundamental (and perhaps more fragile) process of learning which of all the available stimuli are relevant to a decision and should be attended to and learned about. This project proposes to study this "representation learning" process and how it interacts with reward learning, both neurally and computationally. It is hoped that better understanding of representation learning strategies and their neural implementation will allow us to identify how these are affected by drugs of abuse, and will help in developing new treatment strategies targeted at these additional aspects of the disorder.